MCP MeasureMCP Measure

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Feedback Synthesis MCP

Synthesize GitHub Issues, HN and App Store reviews into ranked pain clusters. Pay-per-call x402.

Sollte ich dies verwenden

Qualität und Sicherheit

A
Qualität der Beschreibung
68%
Vollständigkeit des Schemas
98%
Qualität der Benennung
95%
Risiko der Vergiftung
100%
Übereinstimmung der Berechtigungen
100%
Einhaltung des Protokolls
100%

Befunde (3)

  • LOWTool 'synthesize_feedback' description lacks action verbin synthesize_feedback
  • LOWTool 'get_pain_points' description lacks action verbin get_pain_points
  • LOWTool 'get_sentiment_trends' description lacks action verbin get_sentiment_trends

Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.

Kontextkosten

~751Tokens (Tool-Definitionen)
~2.0 KBTypische Antwortgröße
Mittlere Auswirkung auf die Aufmerksamkeit (0.59% von 128k Kontext)

Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.

Installieren

Installation mit einem Klick

Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:

{
  "mcpServers": {
    "feedback-synthesis-mcp": {
      "command": "uvx",
      "args": [
        "feedback-synthesis-mcp"
      ]
    }
  }
}

Ausführbare Pakete

pypifeedback-synthesis-mcp0.1.1stdio

Remote-Endpunkte

https://feedback-synthesis-mcp-production.up.railway.app/mcpstreamable-http

Was es kann

Tool-Inventar

Tools (4)

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⚪synthesize_feedback(sources, focus, max_items_per_source, since)

Multi-source feedback synthesis into ranked pain clusters.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "sources": {
      "items": {
        "additionalProperties": true,
        "type": "object"
      },
      "type": "array",
      "description": "List of source configs. Each must have \"type\" (github_issues, hackernews, appstore) and \"target\" (e.g. \"owner/repo\", \"product name\", app ID)."
    },
    "focus": {
      "default": "",
      "type": "string",
      "description": "Optional focus area to prioritize (e.g. \"performance\", \"onboarding\")."
    },
    "max_items_per_source": {
      "default": 100,
      "type": "integer",
      "description": "Max feedback items per source (default 100)."
    },
    "since": {
      "default": "",
      "type": "string",
      "description": "Only include feedback after this date (ISO 8601, e.g. \"2025-01-01\")."
    }
  },
  "required": [
    "sources"
  ],
  "additionalProperties": false
}

Ausgabe-Schema

{
  "type": "object",
  "additionalProperties": true
}
🟢get_pain_points(source, top_n, max_items)

Quick single-source pain point extraction.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "source": {
      "additionalProperties": true,
      "type": "object",
      "description": "Source config with \"type\" (github_issues, hackernews, appstore) and \"target\"."
    },
    "top_n": {
      "default": 10,
      "type": "integer",
      "description": "Number of top pain points to return (default 10)."
    },
    "max_items": {
      "default": 50,
      "type": "integer",
      "description": "Max feedback items to analyze (default 50)."
    }
  },
  "required": [
    "source"
  ],
  "additionalProperties": false
}

Ausgabe-Schema

{
  "type": "object",
  "additionalProperties": true
}
🟢search_feedback(query, sources, source, target, since, ...)

Full-text search across cached feedback items.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Search query string."
    },
    "sources": {
      "anyOf": [
        {
          "items": {
            "type": "string"
          },
          "type": "array"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional list of source types to filter (e.g. [\"github_issues\"], [\"github\"]).\nAccepted values: github_issues (or \"github\"), hackernews, appstore."
    },
    "source": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Singular alias for sources — accepts a single source type string.\nIf both source and sources are provided, sources takes precedence."
    },
    "target": {
      "default": "",
      "type": "string",
      "description": "Optional target filter (e.g. \"owner/repo\" or app ID)."
    },
    "since": {
      "default": "",
      "type": "string",
      "description": "Only include items after this date (ISO 8601, e.g. \"2025-01-01\")."
    },
    "limit": {
      "default": 20,
      "type": "integer",
      "description": "Max results to return (default 20)."
    }
  },
  "required": [
    "query"
  ],
  "additionalProperties": false
}

Ausgabe-Schema

{
  "type": "object",
  "additionalProperties": true
}
🟢get_sentiment_trends(sources, granularity, since)

Time-series sentiment analysis across feedback sources.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "sources": {
      "items": {
        "additionalProperties": true,
        "type": "object"
      },
      "type": "array",
      "description": "List of source configs. Each must have \"type\" and \"target\"."
    },
    "granularity": {
      "default": "weekly",
      "type": "string",
      "description": "Time granularity — \"weekly\" or \"monthly\" (default \"weekly\")."
    },
    "since": {
      "default": "",
      "type": "string",
      "description": "Only include feedback after this date (ISO 8601)."
    }
  },
  "required": [
    "sources"
  ],
  "additionalProperties": false
}

Ausgabe-Schema

{
  "type": "object",
  "additionalProperties": true
}

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